92 citations · 226 across the 17 of their papers we have counts for
18 papers
An Improved Spectral Conjugate Gradient Algorithm Based on A Modified Wolfe Line Search
Hao Wu, Liping Wang, Hongchao Zhang
In this paper, we combine the th-order Taylor expansion of the objective function with cubic Hermite interpolation conditions. Then, we derive a series of modified secant equati…
A Gradient-Based Implementation of the Polyhedral Active Set Algorithm
William W. Hager, Hongchao Zhang
The Polyhedral Active Set Algorithm (PASA) is designed to optimize a general nonlinear function over a polyhedron. Phase one of the algorithm is a nonmonotone gradient projection a…
Golden ratio primal-dual algorithm with linesearch
Xiaokai Chang, Junfeng Yang, Hongchao Zhang
Golden ratio primal-dual algorithm (GRPDA) is a new variant of the classical Arrow-Hurwicz method for solving structured convex optimization problem, in which the objective functio…
Convergence on a symmetric accelerated stochastic ADMM with larger stepsizes
Jianchao Bai, Deren Han, Hao Sun +1
In this paper, we develop a symmetric accelerated stochastic Alternating Direction Method of Multipliers (SAS-ADMM) for solving separable convex optimization problems with linear c…
An Inexact Accelerated Stochastic ADMM for Separable Convex Optimization
Jianchao Bai, William W. Hager, Hongchao Zhang
An inexact accelerated stochastic Alternating Direction Method of Multipliers (AS-ADMM) scheme is developed for solving structured separable convex optimization problems with linea…
On the acceleration of the Barzilai-Borwein method
Yakui Huang, Yu-Hong Dai, Xin-Wei Liu +1
The Barzilai-Borwein (BB) gradient method is efficient for solving large-scale unconstrained problems to the modest accuracy and has a great advantage of being easily extended to s…
Convergence rates for an inexact ADMM applied to separable convex optimization
William W. Hager, Hongchao Zhang
Convergence rates are established for an inexact accelerated alternating direction method of multipliers (I-ADMM) for general separable convex optimization with a linear constraint…
On the asymptotic convergence and acceleration of gradient methods
Yakui Huang, Yu-Hong Dai, Xin-Wei Liu +1
We consider the asymptotic behavior of a family of gradient methods, which include the steepest descent and minimal gradient methods as special instances. It is proved that each me…
Gradient methods exploiting spectral properties
Yakui Huang, Yu-Hong Dai, Xin-Wei Liu +1
We propose a new stepsize for the gradient method. It is shown that this new stepsize will converge to the reciprocal of the largest eigenvalue of the Hessian, when Dai-Yang's asym…
Generalized Symmetric ADMM for Separable Convex Optimization
Jianchao Bai, Jicheng Li, Fengmin Xu +1
The Alternating Direction Method of Multipliers (ADMM) has been proved to be effective for solving separable convex optimization subject to linear constraints. In this paper, we pr…
A parameterized proximal point algorithm for separable convex optimization
Jianchao Bai, Hongchao Zhang, Jicheng Li
In this paper, we develop a parameterized proximal point algorithm (P-PPA) for solving a class of separable convex programming problems subject to linear and convex constraints. Th…
An Active Set Algorithm for Nonlinear Optimization with Polyhedral Constraints
William W. Hager, Hongchao Zhang
A polyhedral active set algorithm PASA is developed for solving a nonlinear optimization problem whose feasible set is a polyhedron. Phase one of the algorithm is the gradient proj…
Inexact alternating direction multiplier methods for separable convex optimization
William W. Hager, Hongchao Zhang
Inexact alternating direction multiplier methods (ADMMs) are developed for solving general separable convex optimization problems with a linear constraint and with an objective tha…
Generalized Uniformly Optimal Methods for Nonlinear Programming
Saeed Ghadimi, Guanghui Lan, Hongchao Zhang
In this paper, we present a generic framework to extend existing uniformly optimal convex programming algorithms to solve more general nonlinear, possibly nonconvex, optimization p…
Mini-batch Stochastic Approximation Methods for Nonconvex Stochastic Composite Optimization
Saeed Ghadimi, Guanghui Lan, Hongchao Zhang
This paper considers a class of constrained stochastic composite optimization problems whose objective function is given by the summation of a differentiable (possibly nonconvex) c…
A nonmonotone spectral projected gradient method for large-scale topology optimization problems
Ruhollah Tavakoli, Hongchao Zhang
An efficient gradient-based method to solve the volume constrained topology optimization problems is presented. Each iterate of this algorithm is obtained by the projection of a Ba…
An exact algorithm for graph partitioning
William Hager, Dzung Phan, Hongchao Zhang
An exact algorithm is presented for solving edge weighted graph partitioning problems. The algorithm is based on a branch and bound method applied to a continuous quadratic program…
Gradient-based methods for sparse recovery
William Hager, Dzung Phan, Hongchao Zhang
The convergence rate is analyzed for the SpaSRA algorithm (Sparse Reconstruction by Separable Approximation) for minimizing a sum $f (\m{x}) + ψ(\m{x})$ where is smooth and …